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        Download the raw data used to create the plots in this report below:

        Note that additional data was saved in multiqc_data when this report was generated.


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        If you use plots from MultiQC in a publication or presentation, please cite:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

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        Tool Citations

        Please remember to cite the tools that you use in your analysis.

        To help with this, you can download publication details of the tools mentioned in this report:

        About MultiQC

        This report was generated using MultiQC, version 1.13

        You can see a YouTube video describing how to use MultiQC reports here: https://youtu.be/qPbIlO_KWN0

        For more information about MultiQC, including other videos and extensive documentation, please visit http://multiqc.info

        You can report bugs, suggest improvements and find the source code for MultiQC on GitHub: https://github.com/ewels/MultiQC

        MultiQC is published in Bioinformatics:

        MultiQC: Summarize analysis results for multiple tools and samples in a single report
        Philip Ewels, Måns Magnusson, Sverker Lundin and Max Käller
        Bioinformatics (2016)
        doi: 10.1093/bioinformatics/btw354
        PMID: 27312411

        A modular tool to aggregate results from bioinformatics analyses across many samples into a single report.

        This report has been generated by the nf-core/quantms analysis pipeline. For information about how to interpret these results, please see the documentation.

        Report generated on 2022-10-06, 15:09 CST based on data in: /home/qinchunyuan/proteomicsDIA/mzml/pmultiqc_test


        pmultiqc

        pmultiqc is a multiQC module to show the pipeline performance of mass spectrometry based quantification pipelines such as nf-core/quantms.

        Experimental Design

        This table shows the design of the experiment. I.e., which files and channels correspond to which sample/condition/fraction.

        You can see details about it in https://abibuilder.informatik.uni-tuebingen.de/archive/openms/Documentation/release/latest/html/classOpenMS_1_1ExperimentalDesign.html

        Showing 4/4 rows and 6/6 columns.
        Spectra FileFraction_GroupFractionLabelSampleMSstats_ConditionMSstats_BioReplicate
        SWATH-TN-10-1.wiff.mzML3112
        breast cancer
        2
        SWATH-TN-10-2.wiff.mzML4112
        breast cancer
        2
        SWATH-TN-9-1.wiff.mzML1111
        breast cancer
        1
        SWATH-TN-9-2.wiff.mzML2111
        breast cancer
        1

        Summary Table

        This table shows the quantms pipeline summary statistics

        This table shows the quantms pipeline summary statistics

        Showing 1/1 rows and 2/2 columns.
        #MS2 Spectra#Peptides Quantified#Proteins Quantified
        534120
        19163
        3629

        Pipeline Result Statistics

        This plot shows the quantms pipeline final result

        This plot shows the quantms pipeline final result. Including Sample Name、Possible Study Variables、identified the number of peptide in the pipeline、 and identified the number of modified peptide in the pipeline, eg. All data in this table are obtained from the out_msstats file. You can also remove the decoy with the remove_decoy parameter.

        Showing 4/4 rows and 7/7 columns.
        Spectra FileSample NameConditionFraction#Peptide IDs#Unambiguous Peptide IDs#Modified Peptide IDs#Protein (group) IDs
        SWATH-TN-10-1.wiff.mzML
        2
        breast cancer
        1
        9270
        9270
        0
        3379
        SWATH-TN-10-2.wiff.mzML
        2
        breast cancer
        1
        11046
        11046
        0
        3706
        SWATH-TN-9-1.wiff.mzML
        1
        breast cancer
        1
        12771
        12771
        0
        3952
        SWATH-TN-9-2.wiff.mzML
        1
        breast cancer
        1
        14471
        14471
        0
        4440

        Number of Peptides Per Protein

        This plot shows the number of peptides per proteins in quantms pipeline final result

        This statistic is extracted from the out_msstats file. Proteins supported by more peptide identifications can constitute more confident results.

        loading..

        Distribution of precursor charges

        This is a bar chart representing the distribution of the precursor ion charges for a given whole experiment.

        This information can be used to identify potential ionization problems including many 1+ charges from an ESI ionization source or an unexpected distribution of charges. MALDI experiments are expected to contain almost exclusively 1+ charged ions. An unexpected charge distribution may furthermore be caused by specific search engine parameter settings such as limiting the search to specific ion charges.

        loading..

        Number of Peaks per MS/MS spectrum

        This chart represents a histogram containing the number of peaks per MS/MS spectrum in a given experiment. This chart assumes centroid data. Too few peaks can identify poor fragmentation or a detector fault, as opposed to a large number of peaks representing very noisy spectra. This chart is extensively dependent on the pre-processing steps performed to the spectra (centroiding, deconvolution, peak picking approach, etc).

        loading..

        Peak Intensity Distribution

        This is a histogram representing the ion intensity vs. the frequency for all MS2 spectra in a whole given experiment. It is possible to filter the information for all, identified and unidentified spectra. This plot can give a general estimation of the noise level of the spectra.

        Generally, one should expect to have a high number of low intensity noise peaks with a low number of high intensity signal peaks. A disproportionate number of high signal peaks may indicate heavy spectrum pre-filtering or potential experimental problems. In the case of data reuse this plot can be useful in identifying the requirement for pre-processing of the spectra prior to any downstream analysis. The quality of the identifications is not linked to this data as most search engines perform internal spectrum pre-processing before matching the spectra. Thus, the spectra reported are not necessarily pre-processed since the search engine may have applied the pre-processing step internally. This pre-processing is not necessarily reported in the experimental metadata.

        loading..

        Peptides Quantification Table

        This plot shows the quantification information of peptides in the final result (mainly the mzTab file).

        The quantification information of peptides is obtained from the MSstats input file. The table shows the quantitative level and distribution of peptides in different study variables, run and peptiforms. The distribution show all the intensity values in a bar plot above and below the average intensity for all the fractions, runs and peptiforms.

        • BestSearchScore: It is equal to 1 - min(Q.Value) for DIA datasets. Then it is equal to 1 - min(best_search_engine_score[1]), which is from best_search_engine_score[1] column in mzTab peptide table for DDA datasets.
        • Average Intensity: Average intensity of each peptide sequence across all conditions with NA=0 or NA ignored.
        • Peptide intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of fractions, and then mean intensity in technical replicates/biological replicates separately. Click Show replicates to switch to bar plots for every replicate.
        Showing 50/50 rows and 7/7 columns.
        PeptideIDPeptideSequenceModificationProteinNameBestSearchScoreAverage Intensitybreast cancerbreast cancer
        1
        AAAAAAAAAAAAAAAASAGGKEAASGPNDS
        nan
        SP9_HUMAN
        0.99941
        3.754
        3.75377
        2
        AAAAAAALQAK
        nan
        RL4_HUMAN
        0.99997
        3.543
        3.54270
        3
        AAAATGTIFTFR
        nan
        IPSP_HUMAN
        0.99968
        3.493
        3.49276
        4
        AAAAVEPDVVVK
        nan
        SELS_HUMAN
        0.99988
        2.913
        2.91328
        5
        AAAAVVAAAAR
        nan
        TRUB1_HUMAN
        0.99658
        2.841
        2.84073
        6
        AAAEELLAR
        nan
        SHIP2_HUMAN
        0.99968
        3.448
        3.44809
        7
        AAAEVNQDYGLDPK
        nan
        FUMH_HUMAN
        0.99988
        3.408
        3.40820
        8
        AAAFEEQENETVVVK
        nan
        TLN1_HUMAN
        0.99988
        3.390
        3.39005
        9
        AAAFEEQENETVVVKEK
        nan
        TLN1_HUMAN
        0.99967
        3.225
        3.22453
        10
        AAAFEQLQK
        nan
        TOM70_HUMAN
        0.99938
        3.330
        3.33031
        11
        AAAITSDILEALGR
        nan
        POSTN_HUMAN
        0.99986
        4.237
        4.23682
        12
        AAAITSDILEALGRDGHFTLFAPTNEAFEK
        nan
        POSTN_HUMAN
        0.99975
        3.487
        3.48703
        13
        AAALAHLDR
        nan
        AOC3_HUMAN
        0.99965
        3.384
        3.38355
        14
        AAALEFLNRFEEAK
        nan
        STIP1_HUMAN
        0.99988
        3.107
        3.10721
        15
        AAALEQFK
        nan
        NNTM_HUMAN
        0.99892
        3.383
        3.38287
        16
        AAALVLQTIWGYK
        nan
        CTND1_HUMAN
        0.99986
        3.272
        3.27161
        17
        AAAMANNLQK
        nan
        RBM39_HUMAN
        0.99418
        3.003
        3.00346
        18
        AAANEQLTR
        nan
        MIC19_HUMAN
        0.99763
        3.155
        3.15473
        19
        AAAPDVAPAPGPAPR
        nan
        HVCN1_HUMAN
        0.99986
        3.133
        3.13322
        20
        AAAQLLQSQAQQSGAQQTK
        nan
        SEP11_HUMAN
        0.99988
        3.375
        3.37511
        21
        AAARPLLTDLYQATMALGYWRAGR
        nan
        PNCB_HUMAN
        0.99955
        3.107
        3.10721
        22
        AAASTDYYK
        nan
        ODPA_HUMAN
        0.99988
        3.059
        3.05931
        23
        AAATGLPEGPAVPVPSR
        nan
        S39AB_HUMAN
        0.99827
        3.586
        3.58580
        24
        AAATLMSER
        nan
        B3AT_HUMAN
        0.99998
        3.498
        3.49772
        25
        AAATMATPLPGR
        nan
        LMNB2_HUMAN
        0.99852
        3.060
        3.05982
        26
        AAATPESQEPQAK
        nan
        MRP_HUMAN
        0.99986
        3.241
        3.24055
        27
        AAATTAQEYLK
        nan
        VILI_HUMAN
        0.99037
        3.108
        3.10755
        28
        AAAVDFTAR
        nan
        ANK3_HUMAN
        0.99398
        2.975
        2.97543
        29
        AAAVGTANKSTVEGIQASVK
        nan
        VINC_HUMAN
        0.99955
        3.509
        3.50880
        30
        AAAVLPVLDLAQR
        nan
        NDUV2_HUMAN
        0.99988
        3.584
        3.58377
        31
        AAAVSLENVLLDVK
        nan
        FMNL3_HUMAN
        0.99398
        3.251
        3.25066
        32
        AAAVSSGFDGAIQLVSLGGR
        nan
        AGRIN_HUMAN
        0.99044
        3.086
        3.08565
        33
        AAAYDKLEK
        nan
        MYH11_HUMAN
        0.99967
        3.148
        3.14829
        34
        AADAEAEVASLNR
        nan
        TPM3_HUMAN
        0.99986
        3.542
        3.54222
        35
        AADAVEDLR
        nan
        PACN2_HUMAN
        0.99989
        3.481
        3.48108
        36
        AADAVEDLRWFR
        nan
        PACN2_HUMAN
        0.99968
        3.222
        3.22220
        37
        AADDTWEPFASGK
        nan
        TTHY_HUMAN
        0.99997
        3.712
        3.71198
        38
        AADEEAFEDNSEEYIR
        nan
        XPO2_HUMAN
        0.99938
        3.470
        3.47041
        39
        AADEEAFEDNSEEYIRR
        nan
        XPO2_HUMAN
        0.99986
        3.394
        3.39387
        40
        AADEVLAEAK
        nan
        CDC73_HUMAN
        0.99786
        3.182
        3.18227
        41
        AADFQLHTHVNDGTEFGGSIYQK
        nan
        VDAC3_HUMAN
        0.99967
        3.304
        3.30449
        42
        AADIDQEVK
        nan
        CAND1_HUMAN
        0.99988
        3.365
        3.36530
        43
        AADISLDNLVEGK
        nan
        TP53B_HUMAN
        0.99389
        3.547
        3.54716
        44
        AADLLVNPLDPR
        nan
        SRPK2_HUMAN
        0.99118
        3.495
        3.49513
        45
        AADMLSGPR
        nan
        AL4A1_HUMAN
        0.99965
        3.313
        3.31345
        46
        AADPPAENSSAPEAEQGGAE
        nan
        YBOX1_HUMAN
        0.99908
        3.487
        3.48735
        47
        AAEAAAAPAESAAPAAGEEPSKEEGEPK
        nan
        BASP1_HUMAN
        0.99967
        3.492
        3.49201
        48
        AAEAAINILK
        nan
        PRKRA_HUMAN
        0.99988
        3.082
        3.08189
        49
        AAEALHGEADSSGVLAAVDATVNK
        nan
        PDIA5_HUMAN
        0.99748
        3.209
        3.20871
        50
        AAEDDEDDDVDTK
        nan
        PTMA_HUMAN
        0.99986
        2.890
        2.89042
        First Page Previous PageNext Page Last PagePage/Total Pages

        Protein Quantification Table

        This plot shows the quantification information of proteins in the final result (mainly the mzTab file).

        The quantification information of proteins is obtained from the msstats input file. The table shows the quantitative level and distribution of proteins in different study variables and run.

        • Peptides_Number: The number of peptides for each protein.
        • Average Intensity: Average intensity of each protein across all conditions with NA=0 or NA ignored.
        • Protein intensity in each condition (Eg. CT=Mixture;CN=UPS1;QY=0.1fmol): Summarize intensity of peptides.

        Click Show replicates to switch to bar plots of quantities in each replicate.

        Showing 50/50 rows and 5/5 columns.
        ProteinIDProteinNamePeptides_NumberAverage Intensitybreast cancerbreast cancer
        1
        1433B_HUMAN
        12
        42
        42.12830
        2
        1433E_HUMAN
        15
        52
        52.21957
        3
        1433F_HUMAN
        7
        23
        23.29043
        4
        1433G_HUMAN
        7
        24
        23.76705
        5
        1433S_HUMAN
        5
        18
        17.69918
        6
        1433T_HUMAN
        5
        17
        16.69628
        7
        1433Z_HUMAN
        19
        68
        68.10224
        8
        2A5D_HUMAN
        3
        10
        9.73872
        9
        2A5E_HUMAN
        1
        3
        2.86064
        10
        2AAA_HUMAN
        16
        53
        53.27789
        11
        2ABA_HUMAN
        6
        19
        19.40441
        12
        3BP1_HUMAN
        4
        13
        12.86064
        13
        3HAO_HUMAN
        2
        6
        6.25778
        14
        3HIDH_HUMAN
        6
        21
        20.56095
        15
        3MG_HUMAN
        2
        6
        5.95900
        16
        4F2_HUMAN
        8
        26
        25.86756
        17
        5NTC_HUMAN
        1
        3
        3.38632
        18
        5NTD_HUMAN
        2
        7
        6.51080
        19
        6PGD_HUMAN
        9
        32
        31.73783
        20
        6PGL_HUMAN
        11
        40
        40.24441
        21
        A16A1_HUMAN
        5
        16
        16.18082
        22
        A1AG1_HUMAN
        5
        18
        17.55442
        23
        A1AG2_HUMAN
        4
        13
        13.04963
        24
        A1AT_HUMAN
        24
        85
        84.78099
        25
        A1BG_HUMAN
        6
        21
        21.15455
        26
        A2AP_HUMAN
        3
        10
        9.76026
        27
        A2GL_HUMAN
        5
        17
        17.13818
        28
        A2MG_HUMAN
        38
        130
        129.86667
        29
        AAAS_HUMAN
        1
        3
        3.34341
        30
        AAAT_HUMAN
        1
        3
        3.15106
        31
        AACS_HUMAN
        4
        13
        13.20837
        32
        AACT_HUMAN
        13
        47
        46.61435
        33
        AAK1_HUMAN
        1
        3
        3.47770
        34
        AAKG1_HUMAN;AAKG2_HUMAN
        1
        3
        3.25648
        35
        AAMDC_HUMAN
        5
        16
        15.55047
        36
        AAPK1_HUMAN
        3
        10
        9.94277
        37
        AATC_HUMAN
        8
        28
        27.65255
        38
        AATM_HUMAN
        8
        27
        27.04918
        39
        AB1IP_HUMAN
        4
        13
        12.58461
        40
        ABCB6_HUMAN
        1
        4
        3.94259
        41
        ABCBA_HUMAN
        1
        3
        2.79934
        42
        ABCD3_HUMAN
        2
        6
        6.22502
        43
        ABCE1_HUMAN
        3
        10
        10.26826
        44
        ABCF1_HUMAN
        7
        22
        22.39400
        45
        ABCF2_HUMAN
        2
        6
        6.26661
        46
        ABCF3_HUMAN
        1
        3
        3.35344
        47
        ABHDA_HUMAN
        3
        10
        9.95513
        48
        ABHDB_HUMAN
        5
        17
        17.15572
        49
        ABHEB_HUMAN
        6
        21
        21.11202
        50
        ABHGA_HUMAN
        1
        3
        3.04870
        First Page Previous PageNext Page Last PagePage/Total Pages

        nf-core/quantms Software Versions

        are collected at run time from the software output.

        Process Name Software Version
        ASSEMBLE_EMPIRICAL_LIBRARY DIA-NN 1.8.1
        CUSTOM_DUMPSOFTWAREVERSIONS python 3.9.5
        yaml 5.4.1
        DIANNCFG sdrf-pipelines 0.0.21
        DIANNCONVERT pmultiqc 0.0.13
        DIANN_PRELIMINARY_ANALYSIS DIA-NN 1.8.1
        INDIVIDUAL_FINAL_ANALYSIS DIA-NN 1.8.1
        MZMLINDEXING FileConverter 2.8.0-pre-exported-20220314 Mar 14 2022, 18:56:49
        SAMPLESHEET_CHECK sdrf-pipelines 0.0.21
        SDRFPARSING sdrf-pipelines 0.0.21
        Workflow Nextflow 21.10.6
        nf-core/quantms 1.1dev

        nf-core/quantms Workflow Summary

        - this information is collected when the pipeline is started.

        Core Nextflow options

        runName
        friendly_edison
        containerEngine
        singularity
        launchDir
        /hps/nobackup/juan/pride/reanalysis/dia-projects/PXD014194
        workDir
        /hps/nobackup/juan/pride/reanalysis/dia-projects/PXD014194/work
        projectDir
        /hps/nobackup/juan/pride/reanalysis/quantms
        userName
        pst_prd
        profile
        ebiclusters
        configFiles
        /hps/nobackup/juan/pride/reanalysis/quantms/nextflow.config, /hps/nobackup/juan/pride/reanalysis/quantms/nextflow.config

        Input/output options

        input
        PXD014194.sdrf.tsv
        outdir
        PXD014194
        email
        yperez@ebi.ac.uk
        root_folder
        /hps/nobackup/juan/pride/reanalysis/dia-projects/PXD014194/

        Protein database

        database
        /hps/nobackup/juan/pride/reanalysis/multiomics-configs/databases/Homo-sapiens-uniprot-reviewed-isoforms-contaminants-202105.fasta

        Database search

        allowed_missed_cleavages
        1
        instrument
        N/A
        max_mods
        1

        Modification localization

        luciphor_debug
        N/A

        PSM re-scoring (general)

        run_fdr_cutoff
        0.10

        PSM re-scoring (Percolator)

        description_correct_features
        N/A

        Consensus ID

        consensusid_considered_top_hits
        N/A
        min_consensus_support
        1

        Isobaric analyzer

        select_activation
        HCD

        Protein Quantification (DDA)

        ratios
        N/A
        normalize
        N/A
        fix_peptides
        N/A

        DIA-NN

        acquisition_method
        N/A
        mass_acc_ms2
        13
        mass_acc_ms1
        7
        scan_window
        8

        Statistical post-processing

        skip_post_msstats
        true
        contrasts
        pairwise

        Quality control

        enable_pmultiqc
        true

        Max job request options

        max_cpus
        48
        max_memory
        300 GB
        max_time
        10d

        Generic options

        email_on_fail
        yperez@ebi.ac.uk
        hostnames
        N/A